En este artículo se presenta una nueva metodología basada en la aplicación de algoritmos Máquina de Soporte Vectorial, Naïve Bayes y Algoritmos Genéticos en diagnósticos de evaluaciones psicosociales para la identificación del grado de riesgo psicosocial en docentes de colegios públicos en Colombia. Se realizó un estudio comparativo del modelo de aprendizaje de máquina para la predicción: Máquinas de Soporte Vectorial (SVM) y Naïve Bayes, en dos etapas, primero con todas las variables y segundo, reduciendo la dimensionalidad de los datos aplicando: algoritmos genéticos. Se seleccionaron las cuarenta mejores variables con mejor eficiencia en la precisión de la predicción. La base de datos utilizada se componía de 3000 registros epidemiológicos, que correspondían a docentes de colegios públicos del área metropolitana de una ciudad colombiana. El uso de SVM reconoce fácilmente variables de tipo fisiológico y el mejor desempeño de predicción se obtuvo con una eficiencia en la precisión del 96,3%.

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.

Este artículo presenta una nueva metodología basada en técnicas de aprendizaje de máquina en diagnósticos de evaluaciones psicosociales para la identificación del grado de riesgo en docentes de colegios públicos en Colombia. Se realizó un estudio comparativo de tres importantes modelos de aprendizaje de máquina para la predicción: redes neuronales artificiales, arboles de decisión y Naïve Bayes, reduciendo la dimensionalidad de los datos. Esto se hace aplicando algoritmos genéticos, algoritmo de la cantidad esperada de la información, algoritmo GainRatioAttributeEval, coeficiente de relación de Pearson y análisis principal de componentes. Se utilizó una base de datos con 5340 registros epidemiológicos, correspondientes a evaluaciones psicosociales de docentes de colegios públicos de una ciudad colombiana. El mejor desempeño de predicción se obtuvo con el modelo de redes neuronales artificiales con una efectividad del 93%.

This paper proposed the application of data mining to identify the psychosocial risk level in teaching elementary, middle y high school education in Colombia. A sample of 1100 data records corresponding to individual assessments were analyzed and processed. The samples were used as input data to the data mining tool called WEKA. Results were compared to assess the performance when applying data mining techniques and classification trees J48 and Naive Bayes. Finally, the application of this predictive tool allows the accuracy 91% compared to the clinic diagnostic. It is concluded that the method can be used tool for preventing the occurrence of psychosocial risk factors in Colombian public school teachers.

BACKGROUND: Occupational accidents in the plumbing activity in the construction sector in developing countries have high rates of work absenteeism. The productivity of enterprises is heavily influenced by it. OBJECTIVE: To propose a model based on the Plan, Do, Check, and Act cycle and data mining for the prevention of occupational accidents in the plumbing activity in the construction sector. METHODS: This cross-sectional study was administered on a total of 200 male technical workers in plumbing. It considers biological, biomechanical, chemical, and, physical risk factors. Three data mining algorithms were compared: Logistic Regression, Naive Bayes, and Decision Trees, classifying the occurrences occupational accident. The model was validated considering 20% of the data collected, maintaining the same proportion between accidents and non-accidents. The model was applied to data collected from the last 17 years of occupational accidents in the plumbing activity in a Colombian construction company. RESULTS: The results showed that, in 90.5% of the cases, the decision tree classifier (J48) correctly identified the possible cases of occupational accidents with the biological, chemical, and, biomechanical, risk factors training variables applied in the model. CONCLUSION: The results of this study are promising in that the model is efficient in predicting the occurrence of an occupational accident in the plumbing activity in the construction sector. For the accidents identified and the associated causes, a plan of measures to mitigate the risk of occupational accidents is proposed.

The study explores how pedagogical reflection on ethics influences dishonest behavior in academia. A quasi-experiment is carried out with 109 industrial engineering students in Colombia, focusing on the phenomenon of corruption in Latin America. The results show an initial positive effect on the recognition and reduction of academic plagiarism, which decreases over time. The study supports the theory of planned behavior by finding that deep deliberation can shape behaviors. In addition, it incorporates the acceptance of guilt as part of the necessary reflection. The study highlights the need to teach ethics in a contextualized manner and through inductive pedagogical mechanisms.